The Decision Rules Mining Algorithm of Information System Based on Rough Set

2011 ◽  
Author(s):  
Hongxin Wan ◽  
Yun Peng
Keyword(s):  
Information System ◽  
Rough Set ◽  
Decision Rules ◽  
Mining Algorithm

scholarly journals Rough Set Approach to Incomplete Multiscale Information System

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Xibei Yang ◽  
Yong Qi ◽  
Dongjun Yu ◽  
Hualong Yu ◽  
Xiaoning Song ◽  
...  
Keyword(s):  
Information System ◽  
Rough Set ◽  
Rough Sets ◽  
Decision Rules ◽  
Structured Data ◽  
Representation System ◽  
New Knowledge ◽  
Real World Applications ◽  
Knowledge Representation System ◽  
Different Levels

Multiscale information system is a new knowledge representation system for expressing the knowledge with different levels of granulations. In this paper, by considering the unknown values, which can be seen everywhere in real world applications, the incomplete multiscale information system is firstly investigated. The descriptor technique is employed to construct rough sets at different scales for analyzing the hierarchically structured data. The problem of unravelling decision rules at different scales is also addressed. Finally, the reduct descriptors are formulated to simplify decision rules, which can be derived from different scales. Some numerical examples are employed to substantiate the conceptual arguments.

scholarly journals A Novel Decision-Making Approach to Fund Investments Based on Multigranulation Rough Set

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Xima Yue ◽  
Xiang Su
Keyword(s):  
Decision Making ◽  
Information System ◽  
Rough Set ◽  
Investment Decision ◽  
Decision Rules ◽  
Investment Project ◽  
Point Of View ◽  
New Approach ◽  
Multigranulation Rough Set ◽  
Fund Investment

Fund investment is a hot issue in today’s society. How to choose a project for investment is affected by many factors. In view of this problem, this paper starts from the granular computing point of view and combines the multigranulation rough set decision-making method to construct a fund investment decision information system; then, the fund investment decision information system is reduced under different thresholds, and the decision rules are extracted through reduction. And from the aspects of decision accuracy and rule accuracy, the rules are analyzed. Finally, decision rules are used to give the decision of the fund investment project. This study provides a new approach to fund management.

scholarly journals Multiple Granulation Rough Set Approach to Interval-Valued Intuitionistic Fuzzy Ordered Information Systems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 949
Author(s):  
Zhen Li ◽  
Xiaoyan Zhang
Keyword(s):  
Information Theory ◽  
Pattern Recognition ◽  
Information System ◽  
Information Systems ◽  
Rough Set ◽  
Fuzzy Set ◽  
Equivalence Relation ◽  
Target Concept ◽  
Actual Case ◽  
Interval Valued

As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which do not apply to the IIFS. In this paper, we combine the IIFS with the ordered information system to obtain the interval-valued intuitive fuzzy ordered information system (IIFOIS). On this basis, three types of multiple granulation rough set models based on the dominance relation are established to effectively overcome the limitation mentioned above, which belongs to the interdisciplinary subject of information theory in mathematics and pattern recognition. First, for an IIFOIS, we put forward a multiple granulation rough set (MGRS) model from two completely symmetry positions, which are optimistic and pessimistic, respectively. Furthermore, we discuss the approximation representation and a few essential characteristics for the target concept, besides several significant rough measures about two kinds of MGRS symmetry models are discussed. Furthermore, a more general MGRS model named the generalized MGRS (GMGRS) model is proposed in an IIFOIS, and some important properties and rough measures are also investigated. Finally, the relationships and differences between the single granulation rough set and the three types of MGRS are discussed carefully by comparing the rough measures between them in an IIFOIS. In order to better utilize the theory to realistic problems, an actual case shows the methods of MGRS models in an IIFOIS is given in this paper.

GRG: KNOWLEDGE DISCOVERY USING INFORMATION GENERALIZATION, INFORMATION REDUCTION, AND RULE GENERATION

1996 ◽  
Vol 05 (01n02) ◽  
pp. 99-112 ◽  
Author(s):  
NING SHAN ◽  
HOWARD J. HAMILTON ◽  
NICK CERCONE
Keyword(s):  
Information System ◽  
Knowledge Discovery ◽  
Relational Databases ◽  
Decision Rules ◽  
Reduction Technique ◽  
Second Step ◽  
Rule Generation ◽  
Information Reduction ◽  
General Rules ◽  
Minimal Subset

We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascen sion technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, a reduction technique is applied to generate a minimalized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.

Incremental Approach for Updating Approximations of Variable Precision Rough Set Based on Dominance Relations

2014 ◽  
Vol 631-632 ◽  
pp. 49-52
Author(s):  
Yan Li ◽  
Jia Jia Hou ◽  
Xiao Qing Liu
Keyword(s):  
Information System ◽  
Rough Set ◽  
Dominance Relation ◽  
Incremental Updating ◽  
Dominance Relations ◽  
Incremental Approach ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Over Time

Variable precision rough set (VPRS) based on dominance relation is an extension of traditional rough set by which can handle preference-ordered information flexibly. This paper focuses on the maintenance of approximations in dominance based VPRS when the objects in an information system vary over time. The incremental updating principles are given as inserting or deleting an object, and some experimental evaluations validates the effectiveness of the proposed method.

scholarly journals Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras

2020 ◽  
pp. 83-88
Author(s):  
Nurhidayat ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Information System ◽  
Rough Set ◽  
Access Point ◽  
Learning Processes ◽  
Discernibility Matrix ◽  
Student Work ◽  
Hard Drives ◽  
Good Shape ◽  
Equivalency Class

Hardware is a computer that can be seen and touched in person. Hardware is used to support student work and learning processes. The hardware should always be in good shape. If any damage should be done quickly. The benefits of this study provide a viable level of data against hardware tools. The purpose of this study determines that hardware that is worth using quickly and precisely so easily can be repaired and replaced. Hard-processed action consists of 12 projectors, 2 units of access point, 6 units of monitors, and 20 CPU units. To see the level of appropriateness regarding hard drives requires a rough set algorithm with that stage: information system; Decision system; Equivalency class; Discernibility matrix; Discernibility Matrix module D; Reduction; Generate Rules. The results of the 40 devices of study STMIK Indonesia Padang subtract college have 10 rules of policy on whether the hardware is still viable, repaired or replaced. So using a rough set algorithm is particularly appropriate to apply in a verifiable level of accuracy to fast and precise hardware.

Patent Infringement Risk Analysis Using Rough Set Theory

2013 ◽  
pp. 1225-1251
Author(s):  
Chun-Che Huang ◽  
Tzu-Liang (Bill) Tseng ◽  
Hao-Syuan Lin
Keyword(s):  
Set Theory ◽  
Intellectual Property Rights ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Patent Infringement ◽  
Significant Issue ◽  
Concept Hierarchy ◽  
Monetary Value ◽  
Credibility Index

Patent infringement risk is a significant issue for corporations due to the increased appreciation of intellectual property rights. If a corporation gives insufficient protection to its patents, it may loss both profits from product, and industry competitiveness. Many studies on patent infringement have focused on measuring the patent trend indicators and the patent monetary value. However, very few studies have attempted to develop a categorization mechanism for measuring and evaluating the patent infringement risk, for example, the categorization of the patent infringement cases, then to determine the significant attributes and introduce the infringement decision rules. This study applies Rough Set Theory (RST), which is suitable for processing qualitative information to induce rules to derive significant attributes for categorization of the patent infringement risk. Moreover, through the use of the concept hierarchy and the credibility index, it can be integrated with RST and then enhance application of the finalized decision rules.

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